Power BI Course in Germany

Overview of Power BI components (Desktop, Service, Mobile).

Copilot Introduction: How to use AI-powered features for data exploration

Installing Power BI Desktop and setting up the environment.

Navigating the Power BI interface: ribbons, panes, and views.

Connecting to various data sources (Excel, SQL, Web APIs).

Data profiling, data types, and handling missing values.

Advanced transformations: merging, appending, pivoting, unpivoting data.

Using Power Query Editor for complex data shaping.

Designing star and snowflake schemas.

Creating and managing table relationships.

DAX basics: calculated columns, measures, and quick measures.

Using DAX for basic calculations like SUM, AVERAGE, COUNT.

Advanced DAX functions: CALCULATE, FILTER, ALL, RELATED.

Time intelligence functions for date-based calculations (e.g., YTD, QTD).

Performance optimization techniques: DAX query tuning, minimizing model size (Copilot flags inefficient queries, suggests optimization techniques, and generates reusable patterns).

Building advanced visuals: combination charts, gauges, maps, and custom visuals

Creating dynamic reports with slicers, bookmarks, and drill-through.

Designing for user experience: layout, color schemes, and storytelling.

Best practices for dashboard design and performance.

Publishing reports and dashboards to Power BI Service.

Configuring dataset refresh schedules and managing gateways.

Setting up Row-Level Security (RLS) for data access control.

Using Copilot to generate natural language insights and queries.

Automating data exploration and report generation.

Implementing Copilot for predictive analytics and recommendations.

Utilizing AI visuals like Key Influencers, Decomposition Tree, and Smart Narrative.

Conducting clustering, anomaly detection, and forecasting.

Interactive Q&A with natural language queries.

Overview of Microsoft Fabric: Synapse, Data Factory, Data Lake, and Power BI.

Using OneLake for centralized data storage.

Building Synapse Dataflows for real-time data ingestion (AI automates dataflow creation, pipeline monitoring, and anomaly detection).

Creating data pipelines for ETL processes and integrating with Power BI (manual oversight ensures enterprise architecture and regulatory governance).

Identify 5-7 repeatable BI tasks (data refresh, anomaly detection, dashboard publishing)

Redesign into AI-driven workflows with clear human validation checkpoints

Define decision boundaries between AI automation and manual oversight

Select 3 areas for Agentic AI implementation with productivity, speed, and scale justifications

Identify 2 areas requiring manual control with risk, compliance, and ethical considerations

Develop implementation roadmap with success metrics

Quantify AI productivity improvements (time savings, error reduction, scalability)

Assess risks of autonomous AI operations without human control

Create governance framework balancing automation with strategic oversight